Abstract:
Nowadays, clustering is the important technique for the analysis of
data. There are many clustering algorithms. Among them, Density-Based
Spatial Clustering of Application with Noise (DBSCAN) is useful for
medical domain. Therefore, the clustering of COVID-19 statistic data is
implemented by using DBSCAN method. It is a density-based clustering
algorithm, grows regions with sufficiently high point density into clusters
and discovers cluster of arbitrary shape and size in medical databases. This
system clusters in each country occurs the similar number of COVID-19
cases. Three distance measuring methods namely Euclidean, Manhattan
and Minkowski are used to calculate the distance between each country
and they evaluate the effectiveness of clustering performance in DBSCAN.
The silhouette coefficient is used to measure the goodness of clustering
quality. According to the experiments, using DBSCAN with Euclidean
distance achieved the superior result. This system is implemented by using
Python programming language.
